Providing useful inflow forecasts of the Manantali dam is critical for zonal consumption and agricultural water supply, power production, flood and drought control and management (Shin et al., Meteorol Appl 27:e1827, 2019). Probabilistic approaches through ensemble forecasting systems are often used to provide more rational and useful hydrological information. This paper aims at implementing an ensemble forecasting system at the Senegal River upper the Manantali dam. Rainfall ensemble is obtained through harmonic analysis and an ARIMA stochastic process. Cyclical errors that are within rainfall cyclical behavior from the stochastic modeling are settled and processed using multivariate statistic tools to dress a rainfall ensemble forecast. The rainfall ensemble is used as input to run the HBV-light to product streamflow ensemble forecasts. A number of 61 forecasted rainfall time series are then used to run already calibrated hydrological model to produce hydrological ensemble forecasts called raw ensemble. In addition, the affine kernel dressing method is applied to the raw ensemble to obtain another ensemble. Both ensembles are evaluated using on the one hand deterministic verifications such the linear correlation, the mean error, the mean absolute error and the root-mean-squared error, and on the other hand, probabilistic scores (Brier score, rank probability score and continuous rank probability score) and diagrams (attribute diagram and relative operating characteristics curve). Results are satisfactory as at deterministic than probabilistic scale, particularly considering reliability, resolution and skill of the systems. For both ensembles, correlation between the averages of the members and corresponding observations is about 0.871. In addition, the dressing method globally improved the performances of ensemble forecasting system. Thus, both schemes system can help decision maker of the Manantali dam in water resources management.

中文翻译：

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塞内加尔河流量管理整体预报系统：在曼纳塔利大坝上游应用

提供Manantali大坝的有用流量预报对于区域消费，农业供水，电力生产，洪水和干旱控制与管理至关重要（Shin等人，Meteorol Appl 27：e1827，2019）。通过集合预报系统的概率方法通常用于提供更合理和有用的水文信息。本文旨在在Manantali大坝上方的塞内加尔河上实施整体预报系统。通过谐波分析和ARIMA随机过程获得降雨合奏。随机建模中降雨周期性行为范围内的周期性误差可以通过使用多元统计工具来拟合和处理降雨集合预报来解决和处理。降雨集合用作输入，以运行HBV-light到产品流集合的预报。然后，使用61个预测的降雨时间序列来运行已校准的水文模型，以产生称为原始集合的水文集合预报。另外，将仿射核修整方法应用于原始合奏以获得另一合奏。一方面使用线性相关性，均值误差，均值绝对误差和均方根误差等确定性验证对两个合奏进行评估，另一方面，使用概率性分数（Brier分数，秩概率分数和连续性分数）进行评估等级概率得分）和图表（属性图和相对操作特性曲线）。在确定性比概率规模上，结果令人满意，特别是考虑到系统的可靠性，分辨率和技能。对于两个合奏，成员平均值与相应观察值之间的相关性约为0.871。此外，该修整方法在整体上提高了集成预测系统的性能。因此，这两种方案系统都可以帮助Manantali大坝的决策者进行水资源管理。